Abstract
This chapter demonstrates some basic statistical methods. Since this book is aimed at people who already know Stata, we assume you are already familiar with most of these methods. We briefly list each example test’s goal and assumptions and how to get R to perform them. For more statistical coverage see Dalgaard’s Introductory Statistics with R [9], or Venable and Ripley’s much more advanced Modern Applied Statistics with S [51]. For a comprehensive text that shows Stata and R code being used for the same analysis, see Hilbe’s Logistic Regression Models, [21]. As usual, Stata code duplicating the R examples used throughout the text is found at the end of the chapter.
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References
Peter Dalgaard. Introductory Statistics with R (Statistics and Computing). Springer, 2008.
John Fox. car: Companion to Applied Regression. Available from http://cran.r-project.org, 2009. R package version 1.2-12.
Jr. Frank E. Harrell and with contributions from many other users. Hmisc: Harrell Miscellaneous. Available from http://cran.r-project.org, 2008. R package version 3.5-2.
Joeseph Hilbe. Logistic Regression Models. Chapman & Hall/CRC Press, 2009.
Jose C. Pinheiro and Douglas M. Bates. Mixed Effects Models in S and S-Plus. Springer, 2002.
Gregory R. Warnes. Includes R source code and/or documentation contributed by Ben Bolker, Thomas Lumley, , and Randall C Johnson. gmodels: Various R Programming Tools for Model Fitting. Available from http://CRAN.R-project.org, 2009. R package version 2.15.0.
W. N. Venables. Exegeses on linear models, 1998. Available from http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf.
W. N. Venables and B. D. Ripley. Modern Applied Statistics with S, 4th ed. Springer, 2002.
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Muenchen, R.A., Hilbe, J.M. (2010). Statistics. In: R for Stata Users. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1318-0_17
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